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This guide contains all of the ASC's statistics resources. If you do not see a topic, suggest it through the suggestion box on the Statistics home page.

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In hypothesis testing, there are two important values you should be familiar with: alpha () and beta (). These values are used to determine how meaningful the results of the test are. So, let’s talk about them!

__Alpha__

Alpha is also known as the level of significance. This represents the probability of obtaining your results due to chance. The smaller this value is, the more “unusual” the results, indicating that the sample is from a different population than it’s being compared to, for example.

Alpha also represents your chance of making a **Type I Error**. What’s that? The chance that you reject the null hypothesis when in reality you should fail to reject the null hypothesis. In other words, your sample data indicates that there is a difference when in reality, there is not. Like a false positive.

__Beta__

The other key-value relates to the power of your study. Power refers to your study’s ability to find a difference if there is one. It logically follows that the greater the power, the more meaningful your results are. Beta = 1 – Power.

Beta also represents the chance of making a **Type II Error**. As you may have guessed, this means that you came to the wrong conclusion in your study, but it’s the opposite of a Type I Error. With a Type II Error, you incorrectly fail to reject the null. In simpler terms, the data indicates that there is not a significant difference when in reality there is. Your study failed to capture a significant finding. Like a false negative.

__Examples____:__

Type I Error: Conclude that a man is pregnant, based on the results of a pregnancy test.

Type II Error: Conclude that a woman in her third trimester is not pregnant, based on the results of a pregnancy test.

- Last Updated: Nov 30, 2022 3:21 PM
- URL: https://library.ncu.edu/statsresources
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